Robust Statistics: Theory and Methods (Wiley Series in Probability and Statistics) Review

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(More customer reviews)I do not agree that this text is a sequel to Huber's classic book on robustness. Huber has recently produced a second edition to the book which is more appropriately called a sequel as it mainly updates the first edition.
Maronna, Martin and Yohai do much more here. Hampel's book takes an influence function approach to robustness. Huber's deals more with constrained maximization such as M estimation. Most books on robustness deal with the problems of location and scale and perhaps a little on multivariate robustness. These author's cover all approaches. They provide a good mix of theory and applications. They include multivariate analysis, generalized linear models, regression and time series. Time series robustness is a strength of this book as these authors have contributed a lot to that theory.
In addition to providing a modern and comprehensive outlook on robustness the authors get into the practical issues of computation providing a chapter on numerical algorithms and the another on the implimentation of robustness in the SPlus software.
Outlier detection and removal is another approach to robustness. First you remove outliers and then you perform classical statistical methods on what is left. This is probably not as good of an approach to robustness as the alternatives. But outliers play a role that is measured by influence functions and reducing their influence rather than eliminating them completely is often the goal of a robust procedure.
This is a one-of-a-kind book on robustness and is very much worth having im your library if you are a professional statistician.

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Classical statistical techniques fail to cope well with deviations from a standard distribution. Robust statistical methods take into account these deviations while estimating the parameters of parametric models, thus increasing the accuracy of the inference. Research into robust methods is flourishing, with new methods being developed and different applications considered.
Robust Statistics sets out to explain the use of robust methods and their theoretical justification. It provides an up-to-date overview of the theory and practical application of the robust statistical methods in regression, multivariate analysis, generalized linear models and time series. This unique book:
Enables the reader to select and use the most appropriate robust method for their particular statistical model.
Features computational algorithms for the core methods.
Covers regression methods for data mining applications.
Includes examples with real data and applications using the S-Plus robust statistics library.
Describes the theoretical and operational aspects of robust methods separately, so the reader can choose to focus on one or the other.
Supported by a supplementary website featuring time-limited S-Plus download, along with datasets and S-Plus code to allow the reader to reproduce the examples given in the book.

Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is ideal for researchers, practitioners and graduate students of statistics, electrical, chemical and biochemical engineering, and computer vision. There is also much to benefit researchers from other sciences, such as biotechnology, who need to use robust statistical methods in their work.

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